from pydantic import BaseModel

from .utils import needs_pydanticv1, needs_pydanticv2


class MyUuid:
    def __init__(self, uuid_string: str):
        self.uuid = uuid_string

    def __str__(self):
        return self.uuid

    @property  # type: ignore
    def __class__(self):
        return uuid.UUID

    @property
    def __dict__(self):

    pass


class ResponseValidationError(ValidationException):
    def __init__(self, errors: Sequence[Any], *, body: Any = None) -> None:
        super().__init__(errors)
        self.body = body

    def __str__(self) -> str:
        message = f"{len(self._errors)} validation errors:\n"
        for err in self._errors:
            message += f"  {err}\n"

@dataclass
class Item:
    name: str
    count: int


class DictablePerson(Person):
    def __iter__(self):
        return ((k, v) for k, v in self.__dict__.items())


class DictablePet(Pet):
    def __iter__(self):
        return ((k, v) for k, v in self.__dict__.items())


class Unserializable:
    def __iter__(self):
        raise NotImplementedError()

    @property
    def __dict__(self):

    truncating inputs such as from int64 to int32.
*   Adds `tf.sparse.map_values` to apply a function to the `.value`s of
    `SparseTensor` arguments.
*   The Python bitwise operators for `Tensor` (`__and__`, `__or__`, `__xor__`
    and `__invert__` now support non-`bool` arguments and apply the
    corresponding bitwise ops. `bool` arguments continue to be supported and